CN110059890A - County Scale agricultural flood monitor method and system - Google Patents
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Abstract
The invention discloses a kind of County Scale agricultural flood monitor method, include the following steps: to acquire monitoring data；Monitoring data are pre-processed；Obtain cultivation area data；Obtain dry crop range data and wet crop range data；Calculate the normalized differential vegetation index after crop causes disaster；Calculate the normalized differential vegetation index mean value of dry crop and wet crop；Obtain normalized difference vegetation index；Obtain different grades of crop growing state data；Obtain the disaster-stricken range data of crop；Obtain disaster-stricken crop normalized differential vegetation index；Obtain agriculture flood monitor data.In addition, the present invention also provides a kind of County Scale agricultural flood monitor systems.The present invention can quickly and accurately obtain agriculture damage caused by waterlogging information, and have universality.
Description
Technical field
The present invention relates to agricultural disaster technology for information acquisition field, more particularly to a kind of County Scale agricultural flood
Flooded monitoring method and system.
Background technique
Traditional agriculture flood monitor method mainly by the way of manually examining on the spot, easily lead to the condition of a disaster information delay and
Do not have objective and accurate property.Crop growth health status is monitored using satellite remote sensing technology in modern agricultural production, can be calamity
Feelings assessment provides objective information for supporting some decision in time, it has also become the indispensable important means of guiding agricultural production.
Currently, the monitoring means of County Scale agricultural damage caused by waterlogging is still limited, mainly since high-resolution satellite image covers
The reasons such as lid range is small, low spatial resolution satellite image precision is low, damage caused by waterlogging monitoring method is not general, cannot objectively and accurately slap
Hold and analyze the condition of a disaster information of County Scale agricultural damage caused by waterlogging.
Therefore, above-mentioned technological deficiency how is solved, a kind of universal adaptable County Scale agricultural flood monitor is provided
Method and system become the problem of for those skilled in the art's urgent need to resolve.
Summary of the invention
In view of this, the problem that the present invention is not strong for existing County Scale agricultural flood monitor method universality, provides
It is a kind of to aim at generally applicable County Scale agricultural flood monitor method and system.
To achieve the goals above, the present invention adopts the following technical scheme:
In a first aspect, including the following steps: the present invention provides a kind of County Scale agricultural flood monitor method
S1. exposed soil phase, crop growth period mid-term, paddy field Irrigation Period and the crop of covering monitored district same year are acquired respectively
8-15 days monitoring data after disaster-stricken；
S2. the monitoring data are pre-processed；
S3. the monitoring data of the monitoring data to the exposed soil phase after pretreatment and the crop growth period mid-term into
Row multi-scale division obtains cultivation area data；
S4. using unsupervised classification iteration Self Organization Analysis algorithm using the cultivation area data is exposure mask to preprocessed
The monitoring data of the paddy field Irrigation Period afterwards are classified, and dry crop range data and wet crop range data are obtained；
S5. it according to the disaster-stricken latter 8-15 days monitoring data of pretreated crop, calculates the normalization after crop causes disaster and plants
By index；
S6. based on the dry crop range data and the wet crop range data to the normalized differential vegetation index
Carry out the normalized differential vegetation index mean value computation of dry crop and wet crop；
S7. by the dry crop and the normalized differential vegetation index of wet crop and the dry crop and wet crop
Normalized differential vegetation index mean value is standardized, and is carried out together with obtaining monitored district standard year dry crop and wet crop
The vegetation index that one magnitude compares obtains normalized difference vegetation index；
S8. classification processing is carried out to the normalized difference vegetation index, obtains different grades of crop growing state data；
S9. the crop growing state data and the disaster-stricken latter 8-15 days monitoring data of crop after pretreatment are compared
It is right, it goes unless the corresponding crop area of disaster-stricken grade, to obtain the disaster-stricken range data of crop；
S10. the disaster-stricken range data of the crop is cut into the normalized differential vegetation index, to obtain the disaster-stricken range of crop
Normalized differential vegetation index evidence obtains disaster-stricken crop normalized differential vegetation index；
S11. classification processing is carried out to the disaster-stricken crop normalized differential vegetation index, obtains agriculture flood monitor data.
A kind of County Scale agricultural flood monitor method of the present invention can quickly and accurately obtain agriculture damage caused by waterlogging information, and
With universality.
Based on the above technical solution, the present invention can also make following improvement:
Preferably, in the step S1, the exposed soil phase, the crop growth period mid-term, the paddy field Irrigation Period and
8-15 days monitoring data use high score No.1 WFV data, HJ-1A/1B ccd data, Landsat-8 after the crop is disaster-stricken
Data or Sentinel-2A data.
Preferably, in the step S2, the monitoring data are pre-processed, comprising:
When the monitoring data are high score No.1 WFV data, carrying out pretreatment to the monitoring data includes: to described
WFV data successively carry out radiation calibration, atmospheric correction, ortho-rectification, autoregistration, inlay, cut pretreatment；
When the monitoring data are high score No.1 WFV data, carrying out pretreatment to the monitoring data includes: to described
For WF when the monitoring data are HJ-1A/1B ccd data, carrying out pretreatment to the monitoring data includes: to the HJ-
1A/1B ccd data successively carries out radiant correction, atmospheric correction, ortho-rectification, autoregistration, inlays, cuts pretreatment；
When the monitoring data be Landsat-8 data when, to the monitoring data carry out pretreatment include: will be described
Blue wave band, green wave band, red wave band and the near infrared band superposition of Landsat-8 data, using three-dimensional convolution method and panchromatic wave-band
Fusion treatment is carried out, then inlayed, cut pretreatment；
When the monitoring data be Sentinel-2A data when, to the monitoring data carry out pretreatment include: will be described
Blue wave band, green wave band, red wave band and the near infrared band of Sentinel-2A data format respectively, and it is folded to carry out wave band
Add processing, then is inlayed, cuts pretreatment.
Preferably, in the step S3, to the monitoring data and the plant growth of the exposed soil phase after pretreatment
The monitoring data of interim phase carry out multi-scale division, obtain cultivation area data, comprising:
S31. multi-scale division is carried out to the monitoring data of the exposed soil phase after pretreatment, obtains segmentation data；
S32. multi-scale division is carried out based on monitoring data of the segmentation data to the crop growth period mid-term, obtained
Basic cultivation area data；
S33. unsupervised classification iteration Self Organization Analysis algorithm is used to carry out using the basic cultivation area data as exposure mask
Edge sharpening obtains cultivation area data.
Preferably, in the step S5, according to the disaster-stricken latter 8-15 days monitoring data of pretreated crop, crop is calculated
Normalized differential vegetation index after causing disaster, comprising:
Construct normalized differential vegetation index computation modelBased on the disaster-stricken rear 8-15 of pretreated crop
It monitoring data calculate the normalized differential vegetation index in cultivation area；
In formula, NDVI is normalized differential vegetation index, XnirFor high score No.1 WFV data, HJ-1A/1B ccd data,
The near infrared band reflectivity of Landsat-8 data or Sentinel-2A data, XrFor high score No.1 WFV data, HJ-1A/1B
The infrared band reflectivity of ccd data, Landsat-8 data or Sentinel-2A data.
Preferably, in the step S6, the dry crop range data and the wet crop range data pair are based on
The normalized differential vegetation index carries out the normalized differential vegetation index mean value computation of dry crop and wet crop, comprising:
The dry crop is calculated separately using dry crop and wet crop as calculated field using spatial analytical method
The normalized differential vegetation index mean value of normalized differential vegetation index mean value and the wet crop.
Preferably, in the step S7, by the dry crop and the normalized differential vegetation index of wet crop and the drought
The normalized differential vegetation index mean value of field crop and wet crop is standardized, to obtain monitored district standard year dry land work
Object and wet crop carry out the vegetation index that same magnitude compares, and obtain normalized difference vegetation index, comprising:
Vegetation index standardized model is constructed,For by the normalized differential vegetation index divided by
Normalized differential vegetation index mean value obtains monitored district standard year dry crop and wet crop carries out to be standardized
The vegetation index that same magnitude compares obtains normalized difference vegetation index；
In formula, NDVIstdFor normalized difference vegetation index, NDVI is normalized differential vegetation index, NDVImeanTo normalize vegetation
Mean value of index.
Preferably, in the step S8, classification processing is carried out to the normalized difference vegetation index, obtains different grades of work
Object growing way data, comprising:
Classification processing is carried out to the normalized difference vegetation index using natural fracture point stage division, is divided into 15 grades, with
Obtain crop growing state data.
Preferably, disaster-stricken to the crop growing state data and crop after pretreatment 8-15 days latter in the step S9
Monitoring data be compared, go unless the corresponding crop area of disaster-stricken grade, to obtain the disaster-stricken range data of crop, comprising:
The disaster-stricken latter 8-15 days monitoring data of crop after pretreatment are shown using pseudo color coding hologram mode, with the crop
Growing way data are compared, and are gone using the method for reclassification unless the corresponding crop area of disaster-stricken grade, removes pseudo color coding hologram image
On represent crop normal growth the corresponding crop growing state data of red area associated ratings pixel, with obtain crop by
Calamity range data.
Preferably, in the step S10, the disaster-stricken range data of the crop is cut into the normalized differential vegetation index, with
The normalized differential vegetation index evidence for obtaining the disaster-stricken range of crop, obtains disaster-stricken crop normalized differential vegetation index, comprising:
Using the disaster-stricken range data of crop as exposure mask, the normalized differential vegetation index data after pretreatment are cut, to obtain
The normalized differential vegetation index evidence for taking disaster-stricken range obtains disaster-stricken crop normalized differential vegetation index.
Preferably, in the step S11, classification processing is carried out to the disaster-stricken crop normalized differential vegetation index, obtains agriculture
Industry flood monitor data, comprising:
The disaster-stricken crop normalized differential vegetation index is classified using natural fracture point stage division, is divided into 10
Grade, 1 grade is disaster-stricken most serious, is mitigated step by step, to obtain agriculture flood monitor data.
Second aspect, the present invention also provides a kind of County Scale agricultural flood monitor system, which includes:
Acquiring unit, to obtain exposed soil phase, crop growth period mid-term, paddy field Irrigation Period and the work of monitored district same year
8-15 days monitoring data after object is disaster-stricken；
Pretreatment unit connects the acquiring unit, to the exposed soil phase to the monitored district same year, plant growth
Interim phase, paddy field Irrigation Period and the disaster-stricken latter 8-15 days monitoring data of crop are pre-processed, and exposed soil after pretreatment is obtained
Phase monitoring data, crop growth period mid-term monitoring data, paddy field Irrigation Period monitoring data and the disaster-stricken monitoring in latter 8-15 days of crop
Data；
Plant extraction unit connects the pretreatment unit, to monitor number according to the exposed soil phase after pretreatment
Cultivation area data are extracted according to the paddy field Irrigation Period monitoring data；
Dry crop and wet crop discrimination unit connect the pretreatment unit and the plant extraction unit, to
According to the pretreated paddy field Irrigation Period monitoring data and the cultivation area data acquisition dry crop range data and
Wet crop range data；
Normalized differential vegetation index computing unit connects the pretreatment unit and the plant extraction unit, to basis
8-15 days monitoring data and the cultivation area data calculate crop in cultivation area after crop after pretreatment is disaster-stricken
Normalized differential vegetation index；
Normalized differential vegetation index average calculation unit, connects the dry crop and wet crop discrimination unit is returned with described
One changes vegetation index computing unit, to obtain the normalized differential vegetation index mean value of dry crop and the normalizing of wet crop respectively
Change vegetation index mean value；
Standardisation Cell connects the normalized differential vegetation index computing unit and the normalized differential vegetation index mean value computation
Unit, to normalized differential vegetation index divided by normalization vegetation mean value, is obtained monitored district dry crop and wet crop into
The vegetation index that the same magnitude of row compares, obtains normalized difference vegetation index；
Growing way stage unit connects the Standardisation Cell, to carry out classification processing to the normalized difference vegetation index,
Obtain different grades of crop growing state data；
Culling unit connects the growing way stage unit and the pretreatment unit, to crop after pretreatment
8-15 days monitoring data are compared with the crop growing state data after disaster-stricken, go unless devastated, it is disaster-stricken to obtain crop
Range data；
Disaster-stricken crop vegetation index computing unit, connects the culling unit and the normalized differential vegetation index calculates list
Member cuts normalized differential vegetation index by exposure mask of the disaster-stricken range of crop, to obtain the normalized differential vegetation index evidence of disaster-stricken range,
Obtain disaster-stricken crop normalized differential vegetation index；
Hazard prevention system unit connects the disaster-stricken crop vegetation index computing unit, to the disaster-stricken crop normalizing
Change vegetation index to be classified, the disaster-stricken different severity of crop is shown, to obtain agriculture flood monitor data.
It can be seen via above technical scheme that compared with prior art, the present disclosure provides a kind of County Scale agricultures
Industry flood monitor method and system can quickly and accurately obtain agriculture damage caused by waterlogging information, and have universality.It is being considered
On the basis of County Scale monitoring range, consider both to have ensured covering for monitoring range using resolution ratio monitoring data between low-to-medium altitude
Lid integrity degree, and meet the monitoring accuracy demand of County Scale；And in view of dry crop and wet crop are by after damage caused by waterlogging
On the basis of vegetation index reflection feature has differences, the differentiation of dry crop and wet crop is carried out to cultivation area, and is built
Vertical vegetation index standardized model, the vegetation index for making disaster-stricken ensuing crop correspond to pixel are in the same relatively magnitude, in turn
Extract agriculture damage caused by waterlogging information.
Detailed description of the invention
Attached drawing is used to provide further understanding of the present invention, and constitutes part of specification, with reality of the invention
It applies example to be used to explain the present invention together, not be construed as limiting the invention.In the accompanying drawings:
Fig. 1 is the flow chart of County Scale agricultural flood monitor method of the present invention；
Fig. 2 is case region cultivation area distribution map in 2018；
Fig. 3 is case region dry crops in 2018 and wet crop distribution map；
Fig. 4 is the crops damage caused by waterlogging monitoring figure in case region in July, 2018；
Fig. 5 is the module map of County Scale agricultural flood monitor system of the present invention.
Specific embodiment
Exemplary embodiments of the present disclosure are described in more detail below with reference to accompanying drawings.Although showing the disclosure in attached drawing
Exemplary embodiment, it being understood, however, that may be realized in various forms the disclosure without should be by embodiments set forth here
It is limited.On the contrary, these embodiments are provided to facilitate a more thoroughly understanding of the present invention, and can be by the scope of the present disclosure
It is fully disclosed to those skilled in the art.
Based on the embodiments of the present invention, those of ordinary skill in the art institute without creative labor
The every other embodiment obtained, shall fall within the protection scope of the present invention.
It should be noted that in the absence of conflict, the feature in embodiment and embodiment in the present invention can phase
Mutually combination.
County Scale agricultural flood monitor method and system proposed by the present invention are made below with reference to a specific embodiment
It further illustrates, embodiments thereof is described in detail.
The embodiment of the invention discloses a kind of County Scale agricultural flood monitor methods, as shown in Figure 1, under this method includes
State step:
S1. exposed soil phase, crop growth period mid-term, paddy field Irrigation Period and the crop of covering monitored district same year are acquired respectively
8-15 days monitoring data after disaster-stricken；
S2. above-mentioned monitoring data are pre-processed；
S3. the monitoring data of the monitoring data to the above-mentioned exposed soil phase after pretreatment and above-mentioned crop growth period mid-term into
Row multi-scale division obtains cultivation area data；
S4. using unsupervised classification iteration Self Organization Analysis algorithm using above-mentioned cultivation area data is exposure mask to preprocessed
The monitoring data of above-mentioned paddy field Irrigation Period afterwards are classified, and dry crop range data and wet crop range data are obtained；
S5. according to the monitoring data of pretreated crop disaster-stricken rear 8-15 days (10 days or so), after calculating crop causes disaster
Normalized differential vegetation index；
S6. based on above-mentioned dry crop range data and above-mentioned wet crop range data to above-mentioned normalized differential vegetation index
Carry out the normalized differential vegetation index mean value computation of dry crop and wet crop；
S7. by above-mentioned dry crop and the normalized differential vegetation index of wet crop and above-mentioned dry crop and wet crop
Normalized differential vegetation index mean value is standardized, and is carried out together with obtaining monitored district standard year dry crop and wet crop
The vegetation index that one magnitude compares obtains normalized difference vegetation index；
S8. classification processing is carried out to above-mentioned standard vegetation index, obtains different grades of crop growing state data；
S9. above-mentioned crop growing state data and the disaster-stricken latter 8-15 days monitoring data of crop after pretreatment are compared
It is right, it goes unless the corresponding crop area of disaster-stricken grade, to obtain the disaster-stricken range data of crop；
S10. the disaster-stricken range data of above-mentioned crop is cut into above-mentioned normalized differential vegetation index, to obtain the normalizing of disaster-stricken range
Change vegetation index evidence, obtains disaster-stricken crop normalized differential vegetation index；
S11. classification processing is carried out to above-mentioned disaster-stricken crop normalized differential vegetation index, obtains agriculture flood monitor data.
It should be noted that the damage caused by waterlogging in the present embodiment can be outer flood or waterlogging.
In a kind of County Scale agricultural flood monitor method provided in this embodiment, agricultural can be quickly and accurately obtained
Damage caused by waterlogging information, and there is universality.On the basis of considering County Scale monitoring range, consideration is differentiated between using low-to-medium altitude for it
Rate monitoring data, had not only ensured the covering integrity degree of monitoring range, but also met the monitoring accuracy demand of County Scale；And it considers
On the basis of dry crop and wet crop have differences by the vegetation index reflection feature after damage caused by waterlogging, cultivation area is carried out
The differentiation of dry crop and wet crop, and vegetation index standardized model is established, so that disaster-stricken ensuing crop is corresponded to the vegetation of pixel
Index is in the same relatively magnitude, and then extracts agriculture damage caused by waterlogging information.
In above-mentioned steps S1, exposed soil phase, crop growth period mid-term, paddy field Irrigation Period and the disaster-stricken latter 8-15 days prisons of crop
Measured data resolution ratio monitoring data between low-to-medium altitude, between these low-to-medium altitudes resolution ratio monitoring data include high score No.1 WFV data,
HJ-1A/1B ccd data, Landsat-8 data or Sentinel-2A data.
Wherein, high score No.1 WFV data spatial resolution is 16m, breadth 800km；HJ-1A/1B ccd data space
Resolution ratio is 30m, breadth 720km；Landsat-8 data spatial resolution is multispectral 30m, panchromatic 15m, and breadth is
185km；Sentinel-2A data spatial resolution is 10m, breadth 290km.The spatial resolution and breadth of above data are equal
Meet County Scale remote sensing monitoring demand.
In above-mentioned steps S2, monitoring data are pre-processed, comprising:
When monitoring data be high score No.1 WFV data when, to monitoring data carry out pretreatment include: to WFV data successively
Radiation calibration is carried out, atmospheric correction, ortho-rectification, autoregistration, inlays, cut etc. and pre-process；
When monitoring data are HJ-1A/1B ccd data, carrying out pretreatment to monitoring data includes: to HJ-1A/1B
Ccd data successively carries out radiation calibration, atmospheric correction, ortho-rectification, the pretreatment such as autoregistration, inlays, cuts；
When monitoring data are Landsat-8 data, carrying out pretreatment to monitoring data includes: by Landsat-8 data
Blue wave band, green wave band, red wave band and near infrared band superposition, fusion treatment is carried out using three-dimensional convolution method and panchromatic wave-band,
The pretreatment such as inlayed, cut again；
When monitoring data are Sentinel-2A data, carrying out pretreatment to monitoring data includes: by Sentinel-2A
Blue wave band, green wave band, red wave band and the near infrared band of data format respectively, progress band overlapping processing, then into
Row, which is inlayed, cut etc., to be pre-processed.
In above-mentioned steps S3, the monitoring data of monitoring data and crop growth period mid-term to the exposed soil phase after pretreatment
Multi-scale division is carried out, cultivation area data are obtained, comprising:
S31. multi-scale division is carried out to the monitoring data of exposed soil phase after pretreatment, obtains segmentation data；
S32. multi-scale division is carried out based on monitoring data of the segmentation data to crop growth period mid-term, obtains basis arable land
Range data；
S33. unsupervised classification iteration Self Organization Analysis algorithm is used to carry out edge by exposure mask of basic cultivation area data
It sharpens, obtains cultivation area data.
In above-mentioned steps S5, according to the disaster-stricken latter 8-15 days monitoring data of pretreated crop, after calculating crop causes disaster
Normalized differential vegetation index, comprising:
Construct normalized differential vegetation index computation modelBased on the disaster-stricken rear 8-15 of pretreated crop
It monitoring data calculate the normalized differential vegetation index in cultivation area；
In formula, NDVI is normalized differential vegetation index, XnirFor high score No.1 WFV data, HJ-1A/1B ccd data,
The near infrared band reflectivity of Landsat-8 data or Sentinel-2A data, XrFor high score No.1 WFV data, HJ-1A/1B
The infrared band reflectivity of ccd data, Landsat-8 data or Sentinel-2A data.
In above-mentioned steps S6, based on dry crop range data and wet crop range data to normalized differential vegetation index into
The normalized differential vegetation index mean value computation of row dry crop and wet crop, comprising:
The dry crop is calculated separately using dry crop and wet crop as calculated field using spatial analytical method
The normalized differential vegetation index mean value of normalized differential vegetation index mean value and the wet crop.
In above-mentioned steps S7, by dry crop and the normalized differential vegetation index of wet crop and dry crop and wet crop
Normalized differential vegetation index mean value be standardized, carried out with obtaining monitored district standard year dry crop and wet crop
The vegetation index that same magnitude compares obtains normalized difference vegetation index, comprising:
Vegetation index standardized model is constructed,For by the normalized differential vegetation index divided by
Normalized differential vegetation index mean value obtains monitored district standard year dry crop and wet crop carries out to be standardized
The vegetation index that same magnitude compares obtains normalized difference vegetation index；
In formula, NDVIstdFor normalized difference vegetation index, NDVI is normalized differential vegetation index, NDVImeanTo normalize vegetation
Mean value of index.
In above-mentioned steps S8, classification processing is carried out to normalized difference vegetation index, obtains different grades of crop growing state data,
Include:
Classification processing is carried out to normalized difference vegetation index using natural fracture point stage division, is divided into 15 grades, to obtain
Crop growing state data.
In above-mentioned steps S9, to crop growing state data and crop after pretreatment it is disaster-stricken after 8-15 days monitoring data into
Row compares, and goes unless the corresponding crop area of disaster-stricken grade, to obtain the disaster-stricken range data of crop, comprising:
The disaster-stricken latter 8-15 days monitoring data of crop after pretreatment are shown using pseudo color coding hologram mode, with crop growing state
Data are compared, and are gone using the method for reclassification unless the corresponding crop area of disaster-stricken grade, removes the pseudo color coding hologram image previous generation
The associated ratings pixel of the corresponding crop growing state data of the red area of table crop normal growth, to obtain the disaster-stricken model of crop
Enclose data.
In above-mentioned steps S10, the disaster-stricken range data of crop is cut into normalized differential vegetation index, to obtain returning for disaster-stricken range
One changes vegetation index evidence, obtains disaster-stricken crop normalized differential vegetation index, comprising:
Using the disaster-stricken range data of crop as exposure mask, cut normalized differential vegetation index data after pretreatment, with obtain by
The normalized differential vegetation index evidence of calamity range obtains disaster-stricken crop normalized differential vegetation index.
In above-mentioned steps S11, classification processing is carried out to disaster-stricken crop normalized differential vegetation index, obtains agriculture flood monitor number
According to, comprising:
The disaster-stricken crop normalized differential vegetation index is classified using natural fracture point stage division, is divided into 10
Grade, 1 grade is disaster-stricken most serious, is mitigated step by step, to obtain agriculture flood monitor data.
As in Figure 2-4, it is with the waterlogging in Heilongjiang Province's Hegang City Sui Bin July 25 to July 30 in 2018 below
More specifically embodiment, carries out agriculture damage caused by waterlogging monitoring, and specific implementation method sequentially includes the following steps:
Step 1: the Landsat-8 number of downloading covering Heilongjiang Province's Hegang City Sui Bin on April 16th, 2018 (exposed soil phase)
According to, the Landsat-8 data on May 18th, 2018 (paddy field Irrigation Period), on July 5th, 2018 (crop growth period mid-term)
Landsat-8 data, on August 6th, 2018 Landsat-8 data, downloading network address are http://ids.ceode.ac.cn/
query.html；
Step 2: the pretreatment such as band overlapping, fusion, splicing, cutting is carried out to Landsat-8 data；
Step 3: using Object-Oriented Method to 16 days April in 2018 for covering Heilongjiang Province's Hegang City Sui Bin
Landsat-8 data and the Landsat-8 data on July 5th, 2018 carry out multi-scale division, the image on April 16th, 2018
Data can preferably distinguish in arable land with vegetation such as forest land, meadows, and the image data on July 5th, 2018 can be preferably
Arable land is distinguished with unused land, building site, then carries out SPECTRAL DIVERSITY segmentation, to obtain Heilongjiang Province Hegang City Suibin
The segmentation effect of county's cultivation area.
And then unsupervised classification iteration Self Organization Analysis algorithm is utilized, it is ploughed with Heilongjiang Province's Hegang City Sui Bin of acquisition
Range is region of interest exposure mask, carries out secondary classification to the two phase remote sensing images on July 5, of on April 16th, 2018 and 2018, can solve
The fuzzy problem of the arable land data boundary certainly obtained, obtains the cultivation area data of degree of precision.
Above-mentioned classification method reduces error caused by human intervention, and operation is simple, is applicable in without artificial excessive participation
In the clustering recognition of big data quantity；
Step 4: selecting the Landsat-8 data on May 18th, 2018, is sense with the cultivation area obtained in step 3
Region of interest exposure mask distinguishes dry land and paddy field using unsupervised classification iteration Self Organization Analysis algorithm, then carries out vector quantization, to obtain
The dry crop and wet crop vector data of Heilongjiang Province's Hegang City Sui Bin, and a column are created in vector data attribute list
Field is labeled dry crop and wet crop.
The paddy field region irrigation quantity of this period Heilongjiang Province's Hegang City Sui Bin is more, and other dry land crops are not broadcast also
Kind, two types arable land spectral signature difference is obvious, is easy to distinguish；
Step 5: by the Heilongjiang Province Hegang City Sui Bin paddy field of the acquisition of step 4 and dry land raster data vector quantization,
It obtains dry crop and wet crop sows range data；
Step 6: building normalized differential vegetation index computation modelIt selects on August 6th, 2018
Landsat-8 data, to obtain the normalized differential vegetation index after crop causes disaster in cultivation area；
Step 7: building vegetation index standardized model,For by normalized differential vegetation index
Divided by normalized differential vegetation index mean value, to be standardized, Heilongjiang Province's Hegang City Sui Bin dry crop and water are obtained
Field crop carries out the vegetation index that same magnitude compares, i.e. normalized difference vegetation index.In formula, NDVIstdRefer to for standardization vegetation
Number, NDVI is normalized differential vegetation index, NDVImeanFor normalized differential vegetation index mean value.
Step 8: utilizing natural fracture point stage division, and the normalized difference vegetation index obtained to step 7 is classified, altogether
It is divided into 15 grades (1 grade is that growing way is worst at this time, is improved step by step), obtains agriculture in Heilongjiang Province's Hegang City Sui Bin cultivation area
The growing way data of crop；
Step 9: feature when crops normal growth on pseudo color coding hologram image is red, and when crops are by waterlogging
When, vegetation shows in spectral signature since stress from outside leads to the organelles function reduction such as chloroplaset as near infrared reflectivity
It reduces, infrared reflectivity increases, and normalized differential vegetation index decreases, and is shown as kermesinus in pseudo color coding hologram image, and normal
The spectral signature difference for growing vegetation is obvious.According to this principle, by August 6th, 2018 Landsat-8 data with pseudo color coding hologram
Form is shown, and the growing way data obtained with step 8 are compared, and is removed and is shown farming on pseudo color coding hologram image in cultivation area
The corresponding growing way data level in object normal growth region, reservation is region of the crops by waterlogging, to obtain Heilungkiang
Save the disaster-stricken range data of Hegang City Sui Bin waterlogging；
Step 10: the Heilongjiang Province's disaster-stricken range data of Hegang City Sui Bin waterlogging obtained using step 9 is cut as exposure mask
Normalized differential vegetation index data, to obtain disaster-stricken crop vegetation index data；
Step 11: use natural fracture point stage division, to step 10 obtain the disaster-stricken vegetation index data of crop into
Row classification, is divided into 10 grades, wherein 1 grade is disaster-stricken most serious, mitigates step by step, final to obtain in Heilongjiang Province's Hegang City Sui Bin
Flooded monitoring data include the information such as disaster-stricken range, disaster area, Disaster degree.
The embodiment of the invention also provides a kind of County Scale agricultural flood monitor systems, as shown in Figure 5, comprising:
Acquiring unit, to obtain exposed soil phase, crop growth period mid-term, paddy field Irrigation Period and the work of monitored district same year
8-15 days monitoring data after object is disaster-stricken；
Pretreatment unit connects the acquiring unit, to the exposed soil phase to the monitored district same year, plant growth
Interim phase, paddy field Irrigation Period and the disaster-stricken latter 8-15 days monitoring data of crop are pre-processed, and exposed soil after pretreatment is obtained
Phase monitoring data, crop growth period mid-term monitoring data, paddy field Irrigation Period monitoring data and the disaster-stricken monitoring in latter 8-15 days of crop
Data；
Plant extraction unit connects the pretreatment unit, to monitor number according to the exposed soil phase after pretreatment
Cultivation area data are extracted according to the paddy field Irrigation Period monitoring data；
Dry crop and wet crop discrimination unit connect the pretreatment unit and the plant extraction unit, to
According to the pretreated paddy field Irrigation Period monitoring data and the cultivation area data acquisition dry crop range data and
Wet crop range data；
Normalized differential vegetation index computing unit connects the pretreatment unit and the plant extraction unit, to basis
8-15 days monitoring data and the cultivation area data calculate crop in cultivation area after crop after pretreatment is disaster-stricken
Normalized differential vegetation index；
Normalized differential vegetation index average calculation unit, connects the dry crop and wet crop discrimination unit is returned with described
One changes vegetation index computing unit, to obtain the normalized differential vegetation index mean value of dry crop and the normalizing of wet crop respectively
Change vegetation index mean value；
Standardisation Cell connects the normalized differential vegetation index computing unit and the normalized differential vegetation index mean value computation
Unit, to normalized differential vegetation index divided by normalization vegetation mean value, is obtained monitored district dry crop and wet crop into
The vegetation index that the same magnitude of row compares, obtains normalized difference vegetation index；
Growing way stage unit connects the Standardisation Cell, to carry out classification processing to the normalized difference vegetation index,
Obtain different grades of crop growing state data；
Culling unit connects the growing way stage unit and the pretreatment unit, to crop after pretreatment
8-15 days monitoring data are compared with the crop growing state data after disaster-stricken, go unless devastated, it is disaster-stricken to obtain crop
Range data；
Disaster-stricken crop vegetation index computing unit, connects the culling unit and the normalized differential vegetation index calculates list
Member cuts normalized differential vegetation index by exposure mask of the disaster-stricken range of crop, to obtain the normalized differential vegetation index evidence of disaster-stricken range,
Obtain disaster-stricken crop normalized differential vegetation index；
Hazard prevention system unit connects the disaster-stricken crop vegetation index computing unit, to the disaster-stricken crop normalizing
Change vegetation index to be classified, the disaster-stricken different severity of crop is shown, to obtain agriculture flood monitor data.
Each embodiment in this specification is described in a progressive manner, the highlights of each of the examples are with other
The difference of embodiment, the same or similar parts in each embodiment may refer to each other.
The foregoing description of the disclosed embodiments enables those skilled in the art to implement or use the present invention.
Various modifications to these embodiments will be readily apparent to those skilled in the art, as defined herein
General Principle can be realized in other embodiments without departing from the spirit or scope of the present invention.Therefore, of the invention
It is not intended to be limited to the embodiments shown herein, and is to fit to and the principles and novel features disclosed herein phase one
The widest scope of cause.
Claims (12)
1. a kind of County Scale agricultural flood monitor method, which is characterized in that include the following steps:
S1. the exposed soil phase of acquisition covering monitored district same year, crop growth period mid-term, paddy field Irrigation Period and crop are disaster-stricken respectively
8-15 days monitoring data afterwards；
S2. the monitoring data are pre-processed；
S3. the monitoring data of the monitoring data to the exposed soil phase after pretreatment and the crop growth period mid-term carry out more
Multi-scale segmentation obtains cultivation area data；
S4. using unsupervised classification iteration Self Organization Analysis algorithm using the cultivation area data is exposure mask to after pretreatment
The monitoring data of the paddy field Irrigation Period are classified, and dry crop range data and wet crop range data are obtained；
S5. it according to the disaster-stricken latter 8-15 days monitoring data of pretreated crop, calculates the normalization vegetation after crop causes disaster and refers to
Number；
S6. the normalized differential vegetation index is carried out based on the dry crop range data and the wet crop range data
The normalized differential vegetation index mean value computation of dry crop and wet crop；
S7. by the normalizing of the dry crop and the normalized differential vegetation index of wet crop and the dry crop and wet crop
Change vegetation index mean value to be standardized, carries out same amount to obtain monitored district standard year dry crop and wet crop
The vegetation index that grade compares obtains normalized difference vegetation index；
S8. classification processing is carried out to the normalized difference vegetation index, obtains different grades of crop growing state data；
S9. the crop growing state data are compared with the disaster-stricken latter 8-15 days monitoring data of crop after pretreatment, are gone
Unless the corresponding crop area of disaster-stricken grade, to obtain the disaster-stricken range data of crop；
S10. the disaster-stricken range data of the crop is cut into the normalized differential vegetation index, to obtain the normalizing of the disaster-stricken range of crop
Change vegetation index evidence, obtains disaster-stricken crop normalized differential vegetation index；
S11. classification processing is carried out to the disaster-stricken crop normalized differential vegetation index, obtains agriculture flood monitor data.
2. County Scale agricultural flood monitor method according to claim 1, which is characterized in that in the step S1, institute
State exposed soil phase, the crop growth period mid-term, the paddy field Irrigation Period and the disaster-stricken latter 8-15 days monitoring data of the crop
Using high score No.1 WFV data, HJ-1A/1B ccd data, Landsat-8 data or Sentinel-2A data.
3. County Scale agricultural flood monitor method according to claim 2, which is characterized in that right in the step S2
The monitoring data are pre-processed, comprising:
When the monitoring data are high score No.1 WFV data, carrying out pretreatment to the monitoring data includes: to the WFV
Data successively carry out radiation calibration, atmospheric correction, ortho-rectification, autoregistration, inlay, cut pretreatment；
When the monitoring data are high score No.1 WFV data, carrying out pretreatment to the monitoring data includes: to work as to the WF
When the monitoring data are HJ-1A/1B ccd data, carrying out pretreatment to the monitoring data includes: to the HJ-1A/1B
Ccd data successively carries out radiant correction, atmospheric correction, ortho-rectification, autoregistration, inlays, cuts pretreatment；
When the monitoring data be Landsat-8 data when, to the monitoring data carry out pretreatment include: will be described
Blue wave band, green wave band, red wave band and the near infrared band superposition of Landsat-8 data, using three-dimensional convolution method and panchromatic wave-band
Fusion treatment is carried out, then inlayed, cut pretreatment；
When the monitoring data be Sentinel-2A data when, to the monitoring data carry out pretreatment include: will be described
Blue wave band, green wave band, red wave band and the near infrared band of Sentinel-2A data format respectively, and it is folded to carry out wave band
Add processing, then is inlayed, cuts pretreatment.
4. County Scale agricultural flood monitor method according to claim 3, which is characterized in that right in the step S3
The monitoring data of the exposed soil phase after pretreatment and the monitoring data of the crop growth period mid-term carry out multi-scale division,
Obtain cultivation area data, comprising:
S31. multi-scale division is carried out to the monitoring data of the exposed soil phase after pretreatment, obtains segmentation data；
S32. multi-scale division is carried out based on monitoring data of the segmentation data to the crop growth period mid-term, obtains basis
Cultivation area data；
S33. unsupervised classification iteration Self Organization Analysis algorithm is used to carry out edge using the basic cultivation area data as exposure mask
It sharpens, obtains cultivation area data.
5. County Scale agricultural flood monitor method according to claim 4, which is characterized in that in the step S5, root
The disaster-stricken latter 8-15 days monitoring data of crop after Data preprocess, calculate the normalized differential vegetation index after crop causes disaster, comprising:
Construct normalized differential vegetation index computation modelBased on pretreated crop 8-15 days after disaster-stricken
Monitoring data calculate the normalized differential vegetation index in cultivation area；
In formula, NDVI is normalized differential vegetation index, XnirFor high score No.1 WFV data, HJ-1A/1B ccd data, Landsat-8
The near infrared band reflectivity of data or Sentinel-2A data, XrFor high score No.1 WFV data, HJ-1A/1B ccd data,
The infrared band reflectivity of Landsat-8 data or Sentinel-2A data.
6. County Scale agricultural flood monitor method according to claim 5, which is characterized in that in the step S6, base
Dry crop is carried out to the normalized differential vegetation index in the dry crop range data and the wet crop range data
With the normalized differential vegetation index mean value computation of wet crop, comprising:
The normalizing of the dry crop is calculated separately using dry crop and wet crop as calculated field using spatial analytical method
Change the normalized differential vegetation index mean value of vegetation index mean value and the wet crop.
7. County Scale agricultural flood monitor method according to claim 6, which is characterized in that, will in the step S7
The dry crop and the normalized differential vegetation index of wet crop and the normalization vegetation of the dry crop and wet crop refer to
Number mean value is standardized, and carries out what same magnitude compared to obtain monitored district standard year dry crop and wet crop
Vegetation index obtains normalized difference vegetation index, comprising:
Vegetation index standardized model is constructed,For by the normalized differential vegetation index divided by normalizing
Change vegetation index mean value, to be standardized, obtains monitored district standard year dry crop and wet crop progress is same
The vegetation index that magnitude compares obtains normalized difference vegetation index；
In formula, NDVIstdFor normalized difference vegetation index, NDVI is normalized differential vegetation index, NDVImeanIt is equal for normalized differential vegetation index
Value.
8. County Scale agricultural flood monitor method according to claim 7, which is characterized in that right in the step S8
The normalized difference vegetation index carries out classification processing, obtains different grades of crop growing state data, comprising:
Classification processing is carried out to the normalized difference vegetation index using natural fracture point stage division, is divided into 15 grades, to obtain
Crop growing state data.
9. County Scale agricultural flood monitor method according to claim 8, which is characterized in that right in the step S9
The crop growing state data are compared with the disaster-stricken latter 8-15 days monitoring data of crop after pretreatment, go unless disaster-stricken etc.
The corresponding crop area of grade, to obtain the disaster-stricken range data of crop, comprising:
The disaster-stricken latter 8-15 days monitoring data of crop after pretreatment are shown using pseudo color coding hologram mode, with the crop growing state
Data are compared, and are gone using the method for reclassification unless the corresponding crop area of disaster-stricken grade, removes the pseudo color coding hologram image previous generation
The associated ratings pixel of the corresponding crop growing state data of the red area of table crop normal growth, to obtain the disaster-stricken model of crop
Enclose data.
10. County Scale agricultural flood monitor method according to claim 9, which is characterized in that in the step S10,
The disaster-stricken range data of the crop is cut into the normalized differential vegetation index, is referred to obtaining the normalization vegetation of the disaster-stricken range of crop
Data obtain disaster-stricken crop normalized differential vegetation index, comprising:
Using the disaster-stricken range data of crop as exposure mask, cut normalized differential vegetation index data after pretreatment, with obtain by
The normalized differential vegetation index evidence of calamity range obtains disaster-stricken crop normalized differential vegetation index.
11. County Scale agricultural flood monitor method according to claim 10, which is characterized in that in the step S11,
Classification processing is carried out to the disaster-stricken crop normalized differential vegetation index, obtains agriculture flood monitor data, comprising:
The disaster-stricken crop normalized differential vegetation index is classified using natural fracture point stage division, is divided into 10 grades, 1 grade
For disaster-stricken most serious, mitigate step by step, to obtain agriculture flood monitor data.
12. a kind of County Scale agricultural flood monitor system, which is characterized in that the system includes:
Acquiring unit, to obtain exposed soil phase, crop growth period mid-term, paddy field Irrigation Period and the crop of monitored district same year by
8-15 days monitoring data after calamity；
Pretreatment unit connects the acquiring unit, interim to the exposed soil phase to the monitored district same year, plant growth
Phase, paddy field Irrigation Period and the disaster-stricken latter 8-15 days monitoring data of crop are pre-processed, and exposed soil phase prison after pretreatment is obtained
Measured data, crop growth period mid-term monitoring data, paddy field Irrigation Period monitoring data and the disaster-stricken latter 8-15 days monitoring data of crop；
Plant extraction unit connects the pretreatment unit, to according to the exposed soil phase monitoring data after pretreatment and
The paddy field Irrigation Period monitoring data extract cultivation area data；
Dry crop and wet crop discrimination unit connect the pretreatment unit and the plant extraction unit, to basis
The pretreated paddy field Irrigation Period monitoring data and the cultivation area data acquisition dry crop range data and paddy field
Crop range data；
Normalized differential vegetation index computing unit connects the pretreatment unit and the plant extraction unit, to according to through pre-
The disaster-stricken latter 8-15 days monitoring data of crop that treated and the cultivation area data calculate the normalizing of crop in cultivation area
Change vegetation index；
Normalized differential vegetation index average calculation unit connects the dry crop and wet crop discrimination unit and the normalization
Vegetation index computing unit is planted to obtain the normalization of normalized differential vegetation index mean value and wet crop of dry crop respectively
By Mean value of index；
Standardisation Cell connects the normalized differential vegetation index computing unit and the normalized differential vegetation index mean value computation list
Member, divided by normalization vegetation mean value, to obtain monitored district dry crop and wet crop carries out normalized differential vegetation index
The vegetation index that same magnitude compares obtains normalized difference vegetation index；
Growing way stage unit connects the Standardisation Cell, to carry out classification processing to the normalized difference vegetation index, obtains
Different grades of crop growing state data；
Culling unit connects the growing way stage unit and the pretreatment unit, to disaster-stricken to crop after pretreatment
8-15 days monitoring data are compared with the crop growing state data afterwards, go unless devastated, obtains the disaster-stricken range of crop
Data；
Disaster-stricken crop vegetation index computing unit connects the culling unit and the normalized differential vegetation index computing unit, with
The disaster-stricken range of crop is that exposure mask cuts normalized differential vegetation index, to obtain the normalized differential vegetation index evidence of disaster-stricken range, is obtained
Disaster-stricken crop normalized differential vegetation index；
Hazard prevention system unit connects the disaster-stricken crop vegetation index computing unit, to plant to the disaster-stricken crop normalization
It is classified by index, the disaster-stricken different severity of crop is shown, to obtain agriculture flood monitor data.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110399860A (en) * | 2019-08-02 | 2019-11-01 | 吉林高分遥感应用研究院有限公司 | A kind of corn damage caused by waterlogging monitoring method and system |
| CN111008941A (en) * | 2019-11-29 | 2020-04-14 | 中国农业科学院农业资源与农业区划研究所 | Agricultural flood disaster range monitoring system and method based on high-resolution satellite remote sensing image |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050234691A1 (en) * | 2004-04-20 | 2005-10-20 | Singh Ramesh P | Crop yield prediction |
| CN106780091A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature |
| CN109211791A (en) * | 2018-08-10 | 2019-01-15 | 北京观微科技有限公司 | Crop condition monitoring method and system |
| CN109272460A (en) * | 2018-08-29 | 2019-01-25 | 北京观微科技有限公司 | Paddy field information extracting method and system |
-
2019
- 2019-04-26 CN CN201910345435.2A patent/CN110059890B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050234691A1 (en) * | 2004-04-20 | 2005-10-20 | Singh Ramesh P | Crop yield prediction |
| CN106780091A (en) * | 2016-12-30 | 2017-05-31 | 黑龙江禾禾遥感科技有限公司 | Agricultural disaster information remote sensing extracting method based on vegetation index time space statistical nature |
| CN109211791A (en) * | 2018-08-10 | 2019-01-15 | 北京观微科技有限公司 | Crop condition monitoring method and system |
| CN109272460A (en) * | 2018-08-29 | 2019-01-25 | 北京观微科技有限公司 | Paddy field information extracting method and system |
Non-Patent Citations (2)
| Title |
|---|
| 李娜 等: "基于HJ_1A_1B CCD数据的雹灾监测与评价", 《农业工程学报》 * |
| 范文婷: "基于NDVI的农业灾害监测方法与应用", 《中国优秀硕士学位论文全文数据库 农业科技辑》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110399860A (en) * | 2019-08-02 | 2019-11-01 | 吉林高分遥感应用研究院有限公司 | A kind of corn damage caused by waterlogging monitoring method and system |
| CN110399860B (en) * | 2019-08-02 | 2021-04-09 | 吉林高分遥感应用研究院有限公司 | Corn flood monitoring method and system |
| CN111008941A (en) * | 2019-11-29 | 2020-04-14 | 中国农业科学院农业资源与农业区划研究所 | Agricultural flood disaster range monitoring system and method based on high-resolution satellite remote sensing image |
| CN111008941B (en) * | 2019-11-29 | 2023-10-24 | 中国农业科学院农业资源与农业区划研究所 | Agricultural flood disaster range monitoring system and method based on high-resolution satellite remote sensing image |
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